Machine Learning-Based Seismic Damage Assessment of a Bridge Portfolio in Cohesive Soil

Machine Learning-Based Seismic Damage Assessment of a Bridge Portfolio in Cohesive Soil

This study investigates the application of machine learning (ML) algorithms for seismic damage classification of bridges supported by helical pile foundations in cohesive soils. While ML techniques have shown strong potential in seismic risk modeling, most prior research has focused on regression ta...

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Bibliographic Details
Main Authors: Burak Ozturk, Ahmed Fouad Hussein, Mohamed Hesham El Naggar
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Buildings
Subjects:
seismic damage classification
machine learning
helical piles
cohesive soils
bridge foundations
class imbalance
Online Access:https://www.mdpi.com/2075-5309/15/10/1682
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